城市交通状态检测的机器学习方法

Li-Min Meng, Lu-Sha Han, Hong Peng, Biaobiao Zhang, Ke-Lin Du
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引用次数: 1

摘要

提出一种基于支持向量机(SVM)和多层感知(MLP)的城市交通状态检测方法。将SVM和MLP分类器融合为级联两层分类器,提高了流量状态分类的准确率。然后将级联两层分类器MLP-SVM、单SVM分类器和单MLP分类器融合,进一步提高最终的检测精度。我们还实现了基于证据的分类器的Dempster-Shafer (D-S)理论。最后,提出了训练阶段和实施阶段的融合策略,以提高检测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A machine learning approach to urban traffic state detection
We propose an urban traffic state detection method based on support vector machine (SVM) and multilayer perception (MLP). Fusing the SVM and MLP classifiers into a cascade two-tier classifier improves the accuracy of the traffic state classification. A cascade two-tier classifier MLP-SVM, a single SVM classifier and a single MLP classifier are then fused to further improve the final detection accuracy. We also implement a Dempster-Shafer (D-S) theory of evidence based classifier. Finally, fusion strategies at the training and implementation phases are proposed to improve the detection accuracy.
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